modelzoo.vision.pytorch.unet.layers.Decoder.Decoder#
- class modelzoo.vision.pytorch.unet.layers.Decoder.Decoder[source]#
Bases:
torch.nn.Module- Parameters
in_channels (int) – The input channel dimension before passing through the decoder.
decoder_filters ([int]) – List of filter sizes for each block in the decoder
encoder_filters ([int]) – List of filter sizes for each block in the encoder. Used to calculate correct input channel dimension when concatenating encoder outputs and transpose outputs.
convs_per_block ([str]) – List of conv specifications for each conv in the block.
bias (bool) – Flag to use bias vectors.
norm_layer (nn.Module) – Desired normalization layer.
norm_kwargs (dict) – A dictionary of the arguments to pass to the constructor of the normalization layer.
act (str) – Activation to use.
skip_connect (bool) – Flag for if the model concatenates encoder outputs to decoder inputs.
residual_blocks (bool) – Flag for using residual connections at the end of each block.
downscale_method (str) – Downscaling method at the end of each block.
dropout_rate (float) – The probability that each element is dropped.
Methods
forward- __call__(*args: Any, **kwargs: Any) Any#
Call self as a function.
- __init__(in_channels, decoder_filters, encoder_filters, convs_per_block, bias, norm_layer, norm_kwargs, act='relu', skip_connect=True, residual_blocks=False, downscale_method='max_pool', dropout_rate=0.0, use_conv3d=False)[source]#
- static __new__(cls, *args: Any, **kwargs: Any) Any#